Correr-Zhou

Correr-Zhou / HiFi-Inpaint

Public

[CVPR 2026] Offical implementation of the paper "HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images".

computer-vision cvpr2026 deep-learning diffusion-models generative-ai
58
3
100% credibility
Found Mar 10, 2026 at 53 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A research project presenting HiFi-Inpaint, a technique for creating realistic human-product images by seamlessly inserting product details from reference photos, with code, models, and dataset planned for release.

How It Works

1
🔍 Discover HiFi-Inpaint

You find this cool project while searching for easy ways to create realistic photos of people holding or wearing products.

2
🌐 Visit the project page

Head to the webpage to check out eye-catching examples of products perfectly blended into human photos.

3
✨ Wow, those details!

Get thrilled seeing the teaser images where every tiny product detail stays sharp and true-to-life.

4
📖 Read about the magic

Dive into the description to learn how it keeps product details perfect when adding them to people pics.

5
📄 Grab the research paper

Download the free paper for deeper insights into creating these amazing blended images.

6
⭐ Watch for updates

Star the page to get notified when free tools, examples, and ready-to-use files become available.

🎉 Make your own images

Soon, generate your own stunning, detail-packed photos of people with products for shops or ads.

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Star Growth

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AI-Generated Review

What is HiFi-Inpaint?

HiFi-Inpaint is a reference-based inpainting tool for generating high-fidelity human-product images, where you mask parts of a human photo and use a product reference image to fill it seamlessly while preserving fine details like textures and logos. It tackles the pain of e-commerce visuals where generic AI outputs butcher product fidelity, delivering pro-level composites via a DiT-based pipeline. Backed by a new 40K-image dataset on Hugging Face, it's the official code for a CVPR 2026 paper—check cvpr 2026 github for similar fresh drops like cvpr 2024 papers github.

Why is it gaining traction?

With 49 stars already, it's buzzing in CVPR circles amid cvpr 2026 deadline hype and reddit threads on cvpr 2026 reviews. It stands out by prioritizing pixel-perfect product details over blurry generics, using shared attention for refs and detail losses—users get crisp outputs rivals can't match. Devs dig the promise of open models post-review, fitting cvpr github template trends.

Who should use this?

E-commerce ML engineers generating ad mockups with humans wearing or holding products. AR/VR devs needing realistic try-on visuals without manual Photoshop. Researchers tweaking inpainting for cvpr 2026 workshops or rebuttals via its timeline-aligned release.

Verdict

Hold off—1.0% credibility score reflects a barebones repo with just a README and pending code/dataset drops after internal review. Stars are low, docs are paper-focused, no tests yet; star it for cvpr 2026 dates updates, but wait for inference CLI/models before production bets. (187 words)

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